Correlation Analysis of Electromyogram Signals

An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts the motion style of an opposite limb. These are the important factors inhibiting the practical application of myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram (EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts the signal from the users and maintains good movement recognition performance. At the initial stage the myoelectric signal is extracted from the user by using the data acquisition system. A new set of features describing the movements of user’s is extracted and the user’s features are classifed using SVM classification. The given signal is then compared with the database signal with the accuracy of 90.910 % across all the EMG signals.